RT Journal Article
T1 Multi UAV coverage path planning in urban environments
A1 Muñoz Mendi, Javier
A1 López Palomino, Blanca
A1 Quevedo Vallejo, Fernando
A1 Monje Micharet, Concepción Alicia
A1 Garrido Bullón, Luis Santiago
A1 Moreno Lorente, Luis Enrique
AB Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.
PB MDPI
SN 1424-8220
YR 2021
FD 2021-11-01
LK https://hdl.handle.net/10016/33944
UL https://hdl.handle.net/10016/33944
LA eng
NO This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems.
NO This research was funded by the EUROPEAN COMMISSION: Innovation and Networks Executive Agency (INEA), through the European H2020 LABYRINTH project. Grant agreement H2020-MG-2019-TwoStages-861696.
DS e-Archivo
RD 31 may. 2024